{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ref: https://github.com/dronecrew/px4tools/blob/master/examples/ulog%20analysis.ipynb\n",
    "%matplotlib inline\n",
    "import px4tools\n",
    "from px4tools import pandas\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.rcParams['figure.figsize'] = (10, 5)\n",
    "plt.rcParams['lines.linewidth'] = 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "log_data = px4tools.read_ulog('/home/llhuang/px4_logs/2022-06-04_pzxc/pazhouxincun_2022_06_04_0133.ulg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'DataFrame' object has no attribute 't_vehicle_groundtruth_0__f_q_0_'\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/python3/dist-packages/matplotlib/cbook/__init__.py:1402: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  ndim = x[:, None].ndim\n",
      "/usr/lib/python3/dist-packages/matplotlib/axes/_base.py:276: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  x = x[:, np.newaxis]\n",
      "/usr/lib/python3/dist-packages/matplotlib/axes/_base.py:278: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  y = y[:, np.newaxis]\n"
     ]
    },
    {
     "ename": "AttributeError",
     "evalue": "'DataFrame' object has no attribute 't_vehicle_global_position_0__f_vel_n'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m/home/llhuang/codework/cppwork/testcode/px4_plots/test_ulog_plot.ipynb Cell 3'\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      <a href='vscode-notebook-cell://ssh-remote%2Bnuc/home/llhuang/codework/cppwork/testcode/px4_plots/test_ulog_plot.ipynb#ch0000002vscode-remote?line=1'>2</a>\u001b[0m d_concat_computed \u001b[39m=\u001b[39m px4tools\u001b[39m.\u001b[39mcompute_data(d_concat)\n\u001b[1;32m      <a href='vscode-notebook-cell://ssh-remote%2Bnuc/home/llhuang/codework/cppwork/testcode/px4_plots/test_ulog_plot.ipynb#ch0000002vscode-remote?line=2'>3</a>\u001b[0m px4tools\u001b[39m.\u001b[39mplot_local_position(d_concat_computed)\n\u001b[0;32m----> <a href='vscode-notebook-cell://ssh-remote%2Bnuc/home/llhuang/codework/cppwork/testcode/px4_plots/test_ulog_plot.ipynb#ch0000002vscode-remote?line=3'>4</a>\u001b[0m px4tools\u001b[39m.\u001b[39;49mplot_velocity(d_concat_computed)\n",
      "File \u001b[0;32m/usr/local/lib/python3.8/dist-packages/px4tools-0+unknown-py3.8.egg/px4tools/ulog.py:420\u001b[0m, in \u001b[0;36mplot_velocity\u001b[0;34m(df, plot_groundtruth)\u001b[0m\n\u001b[1;32m    <a href='file:///usr/local/lib/python3.8/dist-packages/px4tools-0%2Bunknown-py3.8.egg/px4tools/ulog.py?line=415'>416</a>\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m    <a href='file:///usr/local/lib/python3.8/dist-packages/px4tools-0%2Bunknown-py3.8.egg/px4tools/ulog.py?line=416'>417</a>\u001b[0m \u001b[39mPlot velocity\u001b[39;00m\n\u001b[1;32m    <a href='file:///usr/local/lib/python3.8/dist-packages/px4tools-0%2Bunknown-py3.8.egg/px4tools/ulog.py?line=417'>418</a>\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m    <a href='file:///usr/local/lib/python3.8/dist-packages/px4tools-0%2Bunknown-py3.8.egg/px4tools/ulog.py?line=418'>419</a>\u001b[0m plt\u001b[39m.\u001b[39mtitle(\u001b[39m'\u001b[39m\u001b[39mvelocity\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[0;32m--> <a href='file:///usr/local/lib/python3.8/dist-packages/px4tools-0%2Bunknown-py3.8.egg/px4tools/ulog.py?line=419'>420</a>\u001b[0m df\u001b[39m.\u001b[39;49mt_vehicle_global_position_0__f_vel_n\u001b[39m.\u001b[39mplot(label\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39mvel_n\u001b[39m\u001b[39m'\u001b[39m, style\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39mr-\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[1;32m    <a href='file:///usr/local/lib/python3.8/dist-packages/px4tools-0%2Bunknown-py3.8.egg/px4tools/ulog.py?line=420'>421</a>\u001b[0m \u001b[39mif\u001b[39;00m plot_groundtruth:\n\u001b[1;32m    <a href='file:///usr/local/lib/python3.8/dist-packages/px4tools-0%2Bunknown-py3.8.egg/px4tools/ulog.py?line=421'>422</a>\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39m'\u001b[39m\u001b[39mvehicle_global_position_groundtruth\u001b[39m\u001b[39m'\u001b[39m \u001b[39min\u001b[39;00m df:\n",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/pandas/core/generic.py:5487\u001b[0m, in \u001b[0;36mNDFrame.__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m   <a href='file:///home/llhuang/.local/lib/python3.8/site-packages/pandas/core/generic.py?line=5479'>5480</a>\u001b[0m \u001b[39mif\u001b[39;00m (\n\u001b[1;32m   <a href='file:///home/llhuang/.local/lib/python3.8/site-packages/pandas/core/generic.py?line=5480'>5481</a>\u001b[0m     name \u001b[39mnot\u001b[39;00m \u001b[39min\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_internal_names_set\n\u001b[1;32m   <a href='file:///home/llhuang/.local/lib/python3.8/site-packages/pandas/core/generic.py?line=5481'>5482</a>\u001b[0m     \u001b[39mand\u001b[39;00m name \u001b[39mnot\u001b[39;00m \u001b[39min\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_metadata\n\u001b[1;32m   <a href='file:///home/llhuang/.local/lib/python3.8/site-packages/pandas/core/generic.py?line=5482'>5483</a>\u001b[0m     \u001b[39mand\u001b[39;00m name \u001b[39mnot\u001b[39;00m \u001b[39min\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_accessors\n\u001b[1;32m   <a href='file:///home/llhuang/.local/lib/python3.8/site-packages/pandas/core/generic.py?line=5483'>5484</a>\u001b[0m     \u001b[39mand\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_info_axis\u001b[39m.\u001b[39m_can_hold_identifiers_and_holds_name(name)\n\u001b[1;32m   <a href='file:///home/llhuang/.local/lib/python3.8/site-packages/pandas/core/generic.py?line=5484'>5485</a>\u001b[0m ):\n\u001b[1;32m   <a href='file:///home/llhuang/.local/lib/python3.8/site-packages/pandas/core/generic.py?line=5485'>5486</a>\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m[name]\n\u001b[0;32m-> <a href='file:///home/llhuang/.local/lib/python3.8/site-packages/pandas/core/generic.py?line=5486'>5487</a>\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mobject\u001b[39;49m\u001b[39m.\u001b[39;49m\u001b[39m__getattribute__\u001b[39;49m(\u001b[39mself\u001b[39;49m, name)\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 't_vehicle_global_position_0__f_vel_n'"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "d_concat = log_data.concat(dt=0.1)\n",
    "d_concat_computed = px4tools.compute_data(d_concat)\n",
    "px4tools.plot_local_position(d_concat_computed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "916dbcbb3f70747c44a77c7bcd40155683ae19c65e1c03b4aa3499c5328201f1"
  },
  "kernelspec": {
   "display_name": "Python 3.8.10 64-bit",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
